code: def __call__(self, img):
      ori_im = img.copy()
      data = {"image": img}
      data = transform(data, self.preprocess_op)
      if data[0] is None:
          return None, 0
      starttime = time.time()
  
      for idx in range(len(data)):
          if isinstance(data[idx], np.ndarray):
              data[idx] = np.expand_dims(data[idx], axis=0)
          else:
              data[idx] = [data[idx]]
  
      for idx in range(len(self.input_tensor)):
          self.input_tensor[idx].copy_from_cpu(data[idx])
  
      self.predictor.run()
  
      outputs = []
      for output_tensor in self.output_tensors:
          output = output_tensor.copy_to_cpu()
          outputs.append(output)
      preds = outputs[0]
  
      post_result = self.postprocess_op(
          preds, segment_offset_ids=data[6], ocr_infos=data[7]
      )
      elapse = time.time() - starttime
      return post_result, data, elapse
  
file_path: PaddleOCR/ppstructure/kie/predict_kie_token_ser.py
imports: import os
  import sys
  import cv2
  import json
  import numpy
  import time
  import tools.infer.utility
  from ppocr.data import create_operators, transform
  from ppocr.postprocess import build_post_process
  from ppocr.utils.logging import get_logger
  from ppocr.utils.visual import draw_ser_results
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppstructure.utility import parse_args
  from paddleocr import PaddleOCR
label_desc: Use image node_id 2 for calling the SerPredictor obj's underlying member method code with example usage: obj.__call__(img) and returns: post_result,  data,  elapse, None,  int